Content Based Recommendations

Overview of the data

In [1]:
#import all the necessary packages.

from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import nltk
import math
import time
import re
import os
import seaborn as sns
from collections import Counter
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity  
from sklearn.metrics import pairwise_distances
from matplotlib import gridspec
from scipy.sparse import hstack
import plotly
import plotly.figure_factory as ff
from plotly.graph_objs import Scatter, Layout

plotly.offline.init_notebook_mode(connected=True)
warnings.filterwarnings("ignore")
In [33]:
# we have give a json file which consists of all information about
# the products
# loading the data using pandas' read_json file.
data = pd.read_json('tops_fashion.json')
In [34]:
print ('Number of data points : ', data.shape[0], \
       'Number of features/variables:', data.shape[1])
Number of data points :  183138 Number of features/variables: 19
In [35]:
# each product/item has 19 features in the raw dataset.
data.columns # prints column-names or feature-names.
Out[35]:
Index(['asin', 'author', 'availability', 'availability_type', 'brand', 'color',
       'editorial_reivew', 'editorial_review', 'formatted_price',
       'large_image_url', 'manufacturer', 'medium_image_url', 'model',
       'product_type_name', 'publisher', 'reviews', 'sku', 'small_image_url',
       'title'],
      dtype='object')

Of these 19 features, we will be using only 6 features.

1. asin  ( Amazon standard identification number)
2. brand ( brand to which the product belongs to )
3. color ( Color information of apparel, it can contain many colors as   a value ex: red and black stripes ) 
4. product_type_name (type of the apperal, ex: SHIRT/TSHIRT )
5. medium_image_url  ( url of the image )
6. title (title of the product.)
7. formatted_price (price of the product)
In [36]:
data = data[['asin', 'brand', 'color', 'medium_image_url', 'product_type_name', 'title', 'formatted_price']]
In [37]:
print ('Number of data points : ', data.shape[0], \
       'Number of features:', data.shape[1])
data.head() # prints the top rows in the table.
Number of data points :  183138 Number of features: 7
Out[37]:
asin brand color medium_image_url product_type_name title formatted_price
0 B016I2TS4W FNC7C None https://images-na.ssl-images-amazon.com/images... SHIRT Minions Como Superheroes Ironman Long Sleeve R... None
1 B01N49AI08 FIG Clothing None https://images-na.ssl-images-amazon.com/images... SHIRT FIG Clothing Womens Izo Tunic None
2 B01JDPCOHO FIG Clothing None https://images-na.ssl-images-amazon.com/images... SHIRT FIG Clothing Womens Won Top None
3 B01N19U5H5 Focal18 None https://images-na.ssl-images-amazon.com/images... SHIRT Focal18 Sailor Collar Bubble Sleeve Blouse Shi... None
4 B004GSI2OS FeatherLite Onyx Black/ Stone https://images-na.ssl-images-amazon.com/images... SHIRT Featherlite Ladies' Long Sleeve Stain Resistan... $26.26

[5.1] Missing data for various features.

Basic stats for the feature: product_type_name

In [38]:
# We have total 72 unique type of product_type_names
print(data['product_type_name'].describe())

# 91.62% (167794/183138) of the products are shirts,
count     183138
unique        72
top        SHIRT
freq      167794
Name: product_type_name, dtype: object
In [39]:
# names of different product types
print(data['product_type_name'].unique())
['SHIRT' 'SWEATER' 'APPAREL' 'OUTDOOR_RECREATION_PRODUCT'
 'BOOKS_1973_AND_LATER' 'PANTS' 'HAT' 'SPORTING_GOODS' 'DRESS' 'UNDERWEAR'
 'SKIRT' 'OUTERWEAR' 'BRA' 'ACCESSORY' 'ART_SUPPLIES' 'SLEEPWEAR'
 'ORCA_SHIRT' 'HANDBAG' 'PET_SUPPLIES' 'SHOES' 'KITCHEN' 'ADULT_COSTUME'
 'HOME_BED_AND_BATH' 'MISC_OTHER' 'BLAZER' 'HEALTH_PERSONAL_CARE'
 'TOYS_AND_GAMES' 'SWIMWEAR' 'CONSUMER_ELECTRONICS' 'SHORTS' 'HOME'
 'AUTO_PART' 'OFFICE_PRODUCTS' 'ETHNIC_WEAR' 'BEAUTY'
 'INSTRUMENT_PARTS_AND_ACCESSORIES' 'POWERSPORTS_PROTECTIVE_GEAR' 'SHIRTS'
 'ABIS_APPAREL' 'AUTO_ACCESSORY' 'NONAPPARELMISC' 'TOOLS' 'BABY_PRODUCT'
 'SOCKSHOSIERY' 'POWERSPORTS_RIDING_SHIRT' 'EYEWEAR' 'SUIT'
 'OUTDOOR_LIVING' 'POWERSPORTS_RIDING_JACKET' 'HARDWARE' 'SAFETY_SUPPLY'
 'ABIS_DVD' 'VIDEO_DVD' 'GOLF_CLUB' 'MUSIC_POPULAR_VINYL'
 'HOME_FURNITURE_AND_DECOR' 'TABLET_COMPUTER' 'GUILD_ACCESSORIES'
 'ABIS_SPORTS' 'ART_AND_CRAFT_SUPPLY' 'BAG' 'MECHANICAL_COMPONENTS'
 'SOUND_AND_RECORDING_EQUIPMENT' 'COMPUTER_COMPONENT' 'JEWELRY'
 'BUILDING_MATERIAL' 'LUGGAGE' 'BABY_COSTUME' 'POWERSPORTS_VEHICLE_PART'
 'PROFESSIONAL_HEALTHCARE' 'SEEDS_AND_PLANTS' 'WIRELESS_ACCESSORY']
In [40]:
# find the 10 most frequent product_type_names.
product_type_count = Counter(list(data['product_type_name']))
product_type_count.most_common(10)
Out[40]:
[('SHIRT', 167794),
 ('APPAREL', 3549),
 ('BOOKS_1973_AND_LATER', 3336),
 ('DRESS', 1584),
 ('SPORTING_GOODS', 1281),
 ('SWEATER', 837),
 ('OUTERWEAR', 796),
 ('OUTDOOR_RECREATION_PRODUCT', 729),
 ('ACCESSORY', 636),
 ('UNDERWEAR', 425)]

Basic stats for the feature: brand

In [41]:
# there are 10577 unique brands
print(data['brand'].describe())

# 183138 - 182987 = 151 missing values.
count     182987
unique     10577
top         Zago
freq         223
Name: brand, dtype: object
In [42]:
brand_count = Counter(list(data['brand']))
brand_count.most_common(10)
Out[42]:
[('Zago', 223),
 ('XQS', 222),
 ('Yayun', 215),
 ('YUNY', 198),
 ('XiaoTianXin-women clothes', 193),
 ('Generic', 192),
 ('Boohoo', 190),
 ('Alion', 188),
 ('Abetteric', 187),
 ('TheMogan', 187)]

Basic stats for the feature: color

In [43]:
print(data['color'].describe())


# we have 7380 unique colors
# 7.2% of products are black in color
# 64956 of 183138 products have brand information. That's approx 35.4%.
count     64956
unique     7380
top       Black
freq      13207
Name: color, dtype: object
In [44]:
color_count = Counter(list(data['color']))
color_count.most_common(10)
Out[44]:
[(None, 118182),
 ('Black', 13207),
 ('White', 8616),
 ('Blue', 3570),
 ('Red', 2289),
 ('Pink', 1842),
 ('Grey', 1499),
 ('*', 1388),
 ('Green', 1258),
 ('Multi', 1203)]

Basic stats for the feature: formatted_price

In [45]:
 
print(data['formatted_price'].describe())

# Only 28,395 (15.5% of whole data) products with price information
count      28395
unique      3135
top       $19.99
freq         945
Name: formatted_price, dtype: object
In [46]:
price_count = Counter(list(data['formatted_price']))
price_count.most_common(10)
Out[46]:
[(None, 154743),
 ('$19.99', 945),
 ('$9.99', 749),
 ('$9.50', 601),
 ('$14.99', 472),
 ('$7.50', 463),
 ('$24.99', 414),
 ('$29.99', 370),
 ('$8.99', 343),
 ('$9.01', 336)]

Basic stats for the feature: title

In [47]:
print(data['title'].describe())

# All of the products have a title. 
# Titles are fairly descriptive of what the product is. 
# We use titles extensively in this workshop 
# as they are short and informative.
count                                                183138
unique                                               175985
top       Nakoda Cotton Self Print Straight Kurti For Women
freq                                                     77
Name: title, dtype: object
In [48]:
data.to_pickle('pickels/180k_apparel_data')

We save data files at every major step in our processing in "pickle" files.

In [49]:
# consider products which have price information
# data['formatted_price'].isnull() => gives the information 
#about the dataframe row's which have null values price == None|Null
data = data.loc[~data['formatted_price'].isnull()]
print('Number of data points After eliminating price=NULL :', data.shape[0])
Number of data points After eliminating price=NULL : 28395
In [50]:
# consider products which have color information
# data['color'].isnull() => gives the information about the dataframe row's which have null values price == None|Null
data =data.loc[~data['color'].isnull()]
print('Number of data points After eliminating color=NULL :', data.shape[0])
Number of data points After eliminating color=NULL : 28385

We brought down the number of data points from 183K to 28K.

In [51]:
data.to_pickle('pickels/28k_apparel_data')
In [52]:
#To download the images.

'''
from PIL import Image
import requests
from io import BytesIO

for index, row in images.iterrows():
        url = row['large_image_url']
        response = requests.get(url)
        img = Image.open(BytesIO(response.content))
        img.save('images/28k_images/'+row['asin']+'.jpeg')


'''
Out[52]:
"\nfrom PIL import Image\nimport requests\nfrom io import BytesIO\n\nfor index, row in images.iterrows():\n        url = row['large_image_url']\n        response = requests.get(url)\n        img = Image.open(BytesIO(response.content))\n        img.save('workshop/images/28k_images/'+row['asin']+'.jpeg')\n\n\n"

Remove near duplicate items

In [1]:
# read data from pickle file from previous stage
data = pd.read_pickle('pickels/28k_apparel_data')

# find number of products that have duplicate titles.
print(sum(data.duplicated('title')))
# we have 2325 products which have same title but different color
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-99c6fb08224d> in <module>()
      1 # read data from pickle file from previous stage
----> 2 data = pd.read_pickle('pickels/28k_apparel_data')
      3 
      4 # find number of products that have duplicate titles.
      5 print(sum(data.duplicated('title')))

NameError: name 'pd' is not defined

These shirts are exactly same except in size (S, M,L,XL)

:B00AQ4GMCK :B00AQ4GMTS
:B00AQ4GMLQ :B00AQ4GN3I

These shirts exactly same except in color

:B00G278GZ6 :B00G278W6O
:B00G278Z2A :B00G2786X8

In our data there are many duplicate products like the above examples, we need to de-dupe them for better results.

Remove duplicates : Part 1

In [102]:
# read data from pickle file from previous stage
data = pd.read_pickle('pickels/28k_apparel_data')
In [103]:
data.head()
Out[103]:
asin brand color medium_image_url product_type_name title formatted_price
4 B004GSI2OS FeatherLite Onyx Black/ Stone https://images-na.ssl-images-amazon.com/images... SHIRT Featherlite Ladies' Long Sleeve Stain Resistan... $26.26
6 B012YX2ZPI HX-Kingdom Fashion T-shirts White https://images-na.ssl-images-amazon.com/images... SHIRT Women's Unique 100% Cotton T - Special Olympic... $9.99
11 B001LOUGE4 Fitness Etc. Black https://images-na.ssl-images-amazon.com/images... SHIRT Ladies Cotton Tank 2x1 Ribbed Tank Top $11.99
15 B003BSRPB0 FeatherLite White https://images-na.ssl-images-amazon.com/images... SHIRT FeatherLite Ladies' Moisture Free Mesh Sport S... $20.54
21 B014ICEDNA FNC7C Purple https://images-na.ssl-images-amazon.com/images... SHIRT Supernatural Chibis Sam Dean And Castiel Short... $7.50
In [104]:
# Remove All products with very few words in title
data_sorted = data[data['title'].apply(lambda x: len(x.split())>4)]
print("After removal of products with short description:", data_sorted.shape[0])
After removal of products with short description: 27949
In [105]:
# Sort the whole data based on title (alphabetical order of title) 
data_sorted.sort_values('title',inplace=True, ascending=False)
data_sorted.head()
Out[105]:
asin brand color medium_image_url product_type_name title formatted_price
61973 B06Y1KZ2WB Éclair Black/Pink https://images-na.ssl-images-amazon.com/images... SHIRT Éclair Women's Printed Thin Strap Blouse Black... $24.99
133820 B010RV33VE xiaoming Pink https://images-na.ssl-images-amazon.com/images... SHIRT xiaoming Womens Sleeveless Loose Long T-shirts... $18.19
81461 B01DDSDLNS xiaoming White https://images-na.ssl-images-amazon.com/images... SHIRT xiaoming Women's White Long Sleeve Single Brea... $21.58
75995 B00X5LYO9Y xiaoming Red Anchors https://images-na.ssl-images-amazon.com/images... SHIRT xiaoming Stripes Tank Patch/Bear Sleeve Anchor... $15.91
151570 B00WPJG35K xiaoming White https://images-na.ssl-images-amazon.com/images... SHIRT xiaoming Sleeve Sheer Loose Tassel Kimono Woma... $14.32

Some examples of dupliacte titles that differ only in the last few words.

Titles 1:
16. woman's place is in the house and the senate shirts for Womens XXL White
17. woman's place is in the house and the senate shirts for Womens M Grey

Title 2:
25. tokidoki The Queen of Diamonds Women's Shirt X-Large
26. tokidoki The Queen of Diamonds Women's Shirt Small
27. tokidoki The Queen of Diamonds Women's Shirt Large

Title 3:
61. psychedelic colorful Howling Galaxy Wolf T-shirt/Colorful Rainbow Animal Print Head Shirt for woman Neon Wolf t-shirt
62. psychedelic colorful Howling Galaxy Wolf T-shirt/Colorful Rainbow Animal Print Head Shirt for woman Neon Wolf t-shirt
63. psychedelic colorful Howling Galaxy Wolf T-shirt/Colorful Rainbow Animal Print Head Shirt for woman Neon Wolf t-shirt
64. psychedelic colorful Howling Galaxy Wolf T-shirt/Colorful Rainbow Animal Print Head Shirt for woman Neon Wolf t-shirt
In [106]:
indices = []
for i,row in data_sorted.iterrows():
    indices.append(i)
In [107]:
import itertools
stage1_dedupe_asins = []
i = 0
j = 0
num_data_points = data_sorted.shape[0]
while i < num_data_points and j < num_data_points:
    
    previous_i = i

    # store the list of words of ith string in a, ex: a = ['tokidoki', 'The', 'Queen', 'of', 'Diamonds', 'Women's', 'Shirt', 'X-Large']
    a = data['title'].loc[indices[i]].split()

    # search for the similar products sequentially 
    j = i+1
    while j < num_data_points:

        # store the list of words of jth string in b, ex: b = ['tokidoki', 'The', 'Queen', 'of', 'Diamonds', 'Women's', 'Shirt', 'Small']
        b = data['title'].loc[indices[j]].split()

        # store the maximum length of two strings
        length = max(len(a), len(b))

        # count is used to store the number of words that are matched in both strings
        count  = 0

        # itertools.zip_longest(a,b): will map the corresponding words in both strings, it will appened None in case of unequal strings
        # example: a =['a', 'b', 'c', 'd']
        # b = ['a', 'b', 'd']
        # itertools.zip_longest(a,b): will give [('a','a'), ('b','b'), ('c','d'), ('d', None)]
        for k in itertools.zip_longest(a,b): 
            if (k[0] == k[1]):
                count += 1

        # if the number of words in which both strings differ are > 2 , we are considering it as those two apperals are different
        # if the number of words in which both strings differ are < 2 , we are considering it as those two apperals are same, hence we are ignoring them
        if (length - count) > 2: # number of words in which both sensences differ
            # if both strings are differ by more than 2 words we include the 1st string index
            stage1_dedupe_asins.append(data_sorted['asin'].loc[indices[i]])

            # if the comaprision between is between num_data_points, num_data_points-1 strings and they differ in more than 2 words we include both
            if j == num_data_points-1: stage1_dedupe_asins.append(data_sorted['asin'].loc[indices[j]])

            # start searching for similar apperals corresponds 2nd string
            i = j
            break
        else:
            j += 1
    if previous_i == i:
        break
In [108]:
data = data.loc[data['asin'].isin(stage1_dedupe_asins)]

We removed the dupliactes which differ only at the end.

In [109]:
print('Number of data points : ', data.shape[0])
Number of data points :  17593
In [110]:
data.to_pickle('pickels/17k_apperal_data')

Remove duplicates : Part 2


In the previous cell, we sorted whole data in alphabetical order of  titles.Then, we removed titles which are adjacent and very similar title

But there are some products whose titles are not adjacent but very similar.

Examples:

Titles-1
86261.  UltraClub Women's Classic Wrinkle-Free Long Sleeve Oxford Shirt, Pink, XX-Large
115042. UltraClub Ladies Classic Wrinkle-Free Long-Sleeve Oxford Light Blue XXL

TItles-2
75004.  EVALY Women's Cool University Of UTAH 3/4 Sleeve Raglan Tee
109225. EVALY Women's Unique University Of UTAH 3/4 Sleeve Raglan Tees
120832. EVALY Women's New University Of UTAH 3/4-Sleeve Raglan Tshirt

In [65]:
data = pd.read_pickle('pickels/17k_apperal_data')
In [66]:
# This code snippet takes significant amount of time.
# O(n^2) time.

indices = []
for i,row in data.iterrows():
    indices.append(i)

stage2_dedupe_asins = []
while len(indices)!=0:
    i = indices.pop()
    stage2_dedupe_asins.append(data['asin'].loc[i])
    # consider the first apperal's title
    a = data['title'].loc[i].split()
    # store the list of words of ith string in a, ex: a = ['tokidoki', 'The', 'Queen', 'of', 'Diamonds', 'Women's', 'Shirt', 'X-Large']
    for j in indices:
        
        b = data['title'].loc[j].split()
        # store the list of words of jth string in b, ex: b = ['tokidoki', 'The', 'Queen', 'of', 'Diamonds', 'Women's', 'Shirt', 'X-Large']
        
        length = max(len(a),len(b))
        
        # count is used to store the number of words that are matched in both strings
        count  = 0

        # itertools.zip_longest(a,b): will map the corresponding words in both strings, it will appened None in case of unequal strings
        # example: a =['a', 'b', 'c', 'd']
        # b = ['a', 'b', 'd']
        # itertools.zip_longest(a,b): will give [('a','a'), ('b','b'), ('c','d'), ('d', None)]
        for k in itertools.zip_longest(a,b): 
            if (k[0]==k[1]):
                count += 1

        # if the number of words in which both strings differ are < 3 , we are considering it as those two apperals are same, hence we are ignoring them
        if (length - count) < 3:
            indices.remove(j)
In [71]:
# from whole previous products we will consider only 
# the products that are found in previous cell 
data = data.loc[data['asin'].isin(stage2_dedupe_asins)]
In [74]:
print('Number of data points after stage two of dedupe: ',data.shape[0])
# from 17k apperals we reduced to 16k apperals
Number of data points after stage two of dedupe:  16042
In [75]:
data.to_pickle('pickels/16k_apperal_data')
# Storing these products in a pickle file.
# candidates who wants to download these files instead.

6. Text pre-processing

In [2]:
data = pd.read_pickle('pickels/16k_apperal_data')

# NLTK download stop words. [RUN ONLY ONCE]
# In the temrinal, type these commands
# $python3
# $import nltk
# $nltk.download()
In [3]:
# we use the list of stop words that are downloaded from nltk lib.
stop_words = set(stopwords.words('english'))
print ('list of stop words:', stop_words)

def nlp_preprocessing(total_text, index, column):
    if type(total_text) is not int:
        string = ""
        for words in total_text.split():
            # remove the special chars in review like '"#$@!%^&*()_+-~?>< etc.
            word = ("".join(e for e in words if e.isalnum()))
            # Conver all letters to lower-case
            word = word.lower()
            # stop-word removal
            if not word in stop_words:
                string += word + " "
        data[column][index] = string
list of stop words: {'haven', 'such', 'him', 'are', 'into', 'hadn', 'all', 'their', 'that', 'has', 'was', 'just', 'your', 'up', 'both', "that'll", 've', "weren't", 'the', "don't", 'on', 'himself', 'through', 'should', 'these', 'theirs', 'what', 'am', "won't", 'we', 'ma', 'other', 'now', 'below', "should've", 'be', 'between', 'of', "didn't", 'i', 't', "shouldn't", 'from', 'didn', 'herself', 'don', 'further', 'shouldn', 'which', 'm', 'as', 'once', "doesn't", 'you', "wouldn't", "it's", 'yourselves', 'ain', 'in', 'where', 'y', "hasn't", 'needn', 'll', 'during', 'she', 'about', 'few', 'only', 'ourselves', 'this', "mustn't", 'but', 'at', 'until', 'most', 's', 'out', 'themselves', 'by', 'doing', 'above', 'myself', 'over', "wasn't", 'so', 'yours', 'again', "couldn't", "isn't", 'same', 'shan', 'they', 'had', 'were', 'because', 'couldn', 'do', 'down', 'he', 'an', "she's", 'then', "hadn't", 'o', "shan't", 're', 'isn', 'against', 'and', "aren't", 'who', 'being', 'to', 'have', 'does', 'wasn', 'weren', 'wouldn', 'if', 'doesn', 'will', "you're", 'whom', 'more', "mightn't", 'having', 'my', 'very', 'his', 'is', "you'd", 'itself', 'than', 'mustn', 'too', 'been', 'with', 'hers', 'ours', 'hasn', 'here', 'any', 'how', 'it', 'for', "haven't", 'yourself', 'each', 'why', 'can', 'aren', 'there', 'd', 'or', "needn't", 'did', 'off', 'those', 'them', 'after', 'no', "you've", 'not', 'nor', 'own', 'some', 'while', 'before', 'won', "you'll", 'a', 'mightn', 'under', 'me', 'her', 'its', 'our', 'when'}
In [5]:
start_time = time.clock()
# we take each title and we text-preprocess it.
for index, row in data.iterrows():
    nlp_preprocessing(row['title'], index, 'title')
# we print the time it took to preprocess whole titles 
print(time.clock() - start_time, "seconds")
7.31281542484299 seconds
In [6]:
data.head()
Out[6]:
asin brand color medium_image_url product_type_name title formatted_price
4 B004GSI2OS FeatherLite Onyx Black/ Stone https://images-na.ssl-images-amazon.com/images... SHIRT featherlite ladies long sleeve stain resistant... $26.26
6 B012YX2ZPI HX-Kingdom Fashion T-shirts White https://images-na.ssl-images-amazon.com/images... SHIRT womens unique 100 cotton special olympics wor... $9.99
15 B003BSRPB0 FeatherLite White https://images-na.ssl-images-amazon.com/images... SHIRT featherlite ladies moisture free mesh sport sh... $20.54
27 B014ICEJ1Q FNC7C Purple https://images-na.ssl-images-amazon.com/images... SHIRT supernatural chibis sam dean castiel neck tshi... $7.39
46 B01NACPBG2 Fifth Degree Black https://images-na.ssl-images-amazon.com/images... SHIRT fifth degree womens gold foil graphic tees jun... $6.95
In [8]:
data.to_pickle('pickels/16k_apperal_data_preprocessed')

Stemming

In [7]:
from nltk.stem.porter import *
stemmer = PorterStemmer()
print(stemmer.stem('arguing'))
print(stemmer.stem('fishing'))


# We tried using stemming on our titles and it didnot work very well. 
argu
fish

[8] Text based product similarity

In [4]:
data = pd.read_pickle('pickels/16k_apperal_data_preprocessed')
data.head()
Out[4]:
asin brand color medium_image_url product_type_name title formatted_price
4 B004GSI2OS FeatherLite Onyx Black/ Stone https://images-na.ssl-images-amazon.com/images... SHIRT featherlite ladies long sleeve stain resistant... $26.26
6 B012YX2ZPI HX-Kingdom Fashion T-shirts White https://images-na.ssl-images-amazon.com/images... SHIRT womens unique 100 cotton special olympics wor... $9.99
15 B003BSRPB0 FeatherLite White https://images-na.ssl-images-amazon.com/images... SHIRT featherlite ladies moisture free mesh sport sh... $20.54
27 B014ICEJ1Q FNC7C Purple https://images-na.ssl-images-amazon.com/images... SHIRT supernatural chibis sam dean castiel neck tshi... $7.39
46 B01NACPBG2 Fifth Degree Black https://images-na.ssl-images-amazon.com/images... SHIRT fifth degree womens gold foil graphic tees jun... $6.95
In [9]:
# Utility Functions which we will use through the rest of the workshop.


#Display an image
def display_img(url,ax,fig):
    # we get the url of the apparel and download it
    response = requests.get(url)
    img = Image.open(BytesIO(response.content))
    # we will display it in notebook 
    plt.imshow(img)
  
#plotting code to understand the algorithm's decision.
def plot_heatmap(keys, values, labels, url, text):
        # keys: list of words of recommended title
        # values: len(values) ==  len(keys), values(i) represents the occurence of the word keys(i)
        # labels: len(labels) == len(keys), the values of labels depends on the model we are using
                # if model == 'bag of words': labels(i) = values(i)
                # if model == 'tfidf weighted bag of words':labels(i) = tfidf(keys(i))
                # if model == 'idf weighted bag of words':labels(i) = idf(keys(i))
        # url : apparel's url

        # we will devide the whole figure into two parts
        gs = gridspec.GridSpec(2, 2, width_ratios=[4,1], height_ratios=[4,1]) 
        fig = plt.figure(figsize=(25,3))
        
        # 1st, ploting heat map that represents the count of commonly ocurred words in title2
        ax = plt.subplot(gs[0])
        # it displays a cell in white color if the word is intersection(lis of words of title1 and list of words of title2), in black if not
        ax = sns.heatmap(np.array([values]), annot=np.array([labels]))
        ax.set_xticklabels(keys) # set that axis labels as the words of title
        ax.set_title(text) # apparel title
        
        # 2nd, plotting image of the the apparel
        ax = plt.subplot(gs[1])
        # we don't want any grid lines for image and no labels on x-axis and y-axis
        ax.grid(False)
        ax.set_xticks([])
        ax.set_yticks([])
        
        # we call dispaly_img based with paramete url
        display_img(url, ax, fig)
        
        # displays combine figure ( heat map and image together)
        plt.show()
    
def plot_heatmap_image(doc_id, vec1, vec2, url, text, model):

    # doc_id : index of the title1
    # vec1 : input apparels's vector, it is of a dict type {word:count}
    # vec2 : recommended apparels's vector, it is of a dict type {word:count}
    # url : apparels image url
    # text: title of recomonded apparel (used to keep title of image)
    # model, it can be any of the models, 
        # 1. bag_of_words
        # 2. tfidf
        # 3. idf

    # we find the common words in both titles, because these only words contribute to the distance between two title vec's
    intersection = set(vec1.keys()) & set(vec2.keys()) 

    # we set the values of non intersecting words to zero, this is just to show the difference in heatmap
    for i in vec2:
        if i not in intersection:
            vec2[i]=0

    # for labeling heatmap, keys contains list of all words in title2
    keys = list(vec2.keys())
    #  if ith word in intersection(lis of words of title1 and list of words of title2): values(i)=count of that word in title2 else values(i)=0 
    values = [vec2[x] for x in vec2.keys()]
    
    # labels: len(labels) == len(keys), the values of labels depends on the model we are using
        # if model == 'bag of words': labels(i) = values(i)
        # if model == 'tfidf weighted bag of words':labels(i) = tfidf(keys(i))
        # if model == 'idf weighted bag of words':labels(i) = idf(keys(i))

    if model == 'bag_of_words':
        labels = values
    elif model == 'tfidf':
        labels = []
        for x in vec2.keys():
            # tfidf_title_vectorizer.vocabulary_ it contains all the words in the corpus
            # tfidf_title_features[doc_id, index_of_word_in_corpus] will give the tfidf value of word in given document (doc_id)
            if x in  tfidf_title_vectorizer.vocabulary_:
                labels.append(tfidf_title_features[doc_id, tfidf_title_vectorizer.vocabulary_[x]])
            else:
                labels.append(0)
    elif model == 'idf':
        labels = []
        for x in vec2.keys():
            # idf_title_vectorizer.vocabulary_ it contains all the words in the corpus
            # idf_title_features[doc_id, index_of_word_in_corpus] will give the idf value of word in given document (doc_id)
            if x in  idf_title_vectorizer.vocabulary_:
                labels.append(idf_title_features[doc_id, idf_title_vectorizer.vocabulary_[x]])
            else:
                labels.append(0)

    plot_heatmap(keys, values, labels, url, text)


# this function gets a list of wrods along with the frequency of each 
# word given "text"
def text_to_vector(text):
    word = re.compile(r'\w+')
    words = word.findall(text)
    # words stores list of all words in given string, you can try 'words = text.split()' this will also gives same result
    return Counter(words) # Counter counts the occurence of each word in list, it returns dict type object {word1:count}



def get_result(doc_id, content_a, content_b, url, model):
    text1 = content_a
    text2 = content_b
    
    # vector1 = dict{word11:#count, word12:#count, etc.}
    vector1 = text_to_vector(text1)

    # vector1 = dict{word21:#count, word22:#count, etc.}
    vector2 = text_to_vector(text2)

    plot_heatmap_image(doc_id, vector1, vector2, url, text2, model)

[8.2] Bag of Words (BoW) on product titles.

In [12]:
def bag_of_words_model(doc_id, num_results):
    # doc_id: apparel's id in given corpus
    
    # pairwise_dist will store the distance from given input apparel to all remaining apparels
    # the metric we used here is cosine, the coside distance is mesured as K(X, Y) = <X, Y> / (||X||*||Y||)
    # http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity
    pairwise_dist = pairwise_distances(title_features,title_features[doc_id])
    
    # np.argsort will return indices of the smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])
    
    for i in range(0,len(indices)):
        # we will pass 1. doc_id, 2. title1, 3. title2, url, model
        get_result(indices[i],data['title'].loc[df_indices[0]], data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], 'bag_of_words')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print ('Brand:', data['brand'].loc[df_indices[i]])
        print ('Title:', data['title'].loc[df_indices[i]])
        print ('Euclidean similarity with the query image :', pdists[i])
        print('='*60)

#call the bag-of-words model for a product to get similar products.
bag_of_words_model(931, 20) # change the index if you want to.
# In the output heat map each value represents the count value 
# of the label word, the color represents the intersection 
# with inputs title.

#tried 12566
#tried 931
ASIN : B00KLHUIBS
Brand: Anna-Kaci
Title: annakaci sm fit blue green polka dot tie front ruffle trim blouse 
Euclidean similarity with the query image : 0.0
============================================================
ASIN : B0759G15ZX
Brand: Anna-Kaci
Title: annakaci sm fit blue cord ruffle trim tiered hem drop waist denim blouse 
Euclidean similarity with the query image : 3.31662479036
============================================================
ASIN : B00YQ8S4K0
Brand: Anna-Kaci
Title: anna kaci sm fit blue tiedye white printed bohemian ruffle trim blouse 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B00O194W8W
Brand: Anna-Kaci
Title: annakaci sm fit black scallop pattern crochet lace tiered ruffle trim blouse 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B074TLHLMN
Brand: Proenza Schouler
Title: proenza schouler black polka dot blouse 2 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B074F5BP5F
Brand: On Twelfth
Title: twelfth womens blouse blue 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B016P80OKQ
Brand: Studio M
Title: studio blue blouse size 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B007KSG42S
Brand: Anna-Kaci
Title: annakaci sm fit salmon asianinspired chains pleated ruffle ribbon blouse 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B072VHTT1D
Brand: Pleione
Title: pleione womens small scoopneck ruffle trim blouse blue 
Euclidean similarity with the query image : 3.46410161514
============================================================
ASIN : B07111HHX6
Brand: Mossimo
Title: mossimo womens tie front blouse white small 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B01N3SAT1F
Brand: Ganesh
Title: ganesh womens silkblend blouse 2 blue 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B06WW5C6NJ
Brand: Nine West
Title: nine west womens tie front floral print blouse blue l 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B01E1QD5PK
Brand: CHASER
Title: shoulder peasant blouse 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B00G5RYY18
Brand: Anna-Kaci
Title: annakaci sm fit star pattern wide sleeve long blouse 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B06XCZGQLP
Brand: Velvet by Graham & Spencer
Title: velvet womens lace ruffle trim blouse navy 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B00DW1NKSS
Brand: Anna-Kaci
Title: annakaci sm fit white floral lace trim drawstring tie waist pleat front top 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B00HCNNOJW
Brand: Anna-Kaci
Title: annakaci sm fit knife pleat neckline ruffle edge poncho style blouse 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B071NDX99J
Brand: CBK
Title: cbk  blouse fanny  women   blue 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B00886YXL0
Brand: Ageless Patterns
Title: 1893 blouse surplice front pattern 
Euclidean similarity with the query image : 3.60555127546
============================================================
ASIN : B008Z5ST3C
Brand: Anna-Kaci
Title: annakaci sm fit semisheer pink ls chiffon button blouse w polka dots 
Euclidean similarity with the query image : 3.60555127546
============================================================
In [13]:
from sklearn.feature_extraction.text import CountVectorizer
title_vectorizer = CountVectorizer()
title_features   = title_vectorizer.fit_transform(data['title'])
title_features.get_shape() # get number of rows and columns in feature matrix.
# title_features.shape = #data_points * #words_in_corpus
# CountVectorizer().fit_transform(corpus) returns 
# the a sparase matrix of dimensions #data_points * #words_in_corpus

# What is a sparse vector?

# title_features[doc_id, index_of_word_in_corpus] = number of times the word occured in that doc
Out[13]:
(16042, 12609)

[8.5] TF-IDF based product similarity

In [14]:
tfidf_title_vectorizer = TfidfVectorizer(min_df = 0)
tfidf_title_features = tfidf_title_vectorizer.fit_transform(data['title'])
# tfidf_title_features.shape = #data_points * #words_in_corpus
# CountVectorizer().fit_transform(courpus) returns the a sparase matrix of dimensions #data_points * #words_in_corpus
# tfidf_title_features[doc_id, index_of_word_in_corpus] = tfidf values of the word in given doc
In [15]:
def tfidf_model(doc_id, num_results):
    # doc_id: apparel's id in given corpus
    
    # pairwise_dist will store the distance from given input apparel to all remaining apparels
    # the metric we used here is cosine, the coside distance is mesured as K(X, Y) = <X, Y> / (||X||*||Y||)
    # http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity
    pairwise_dist = pairwise_distances(tfidf_title_features,tfidf_title_features[doc_id])

    # np.argsort will return indices of 9 smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the 9 smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])

    for i in range(0,len(indices)):
        # we will pass 1. doc_id, 2. title1, 3. title2, url, model
        get_result(indices[i], data['title'].loc[df_indices[0]], data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], 'tfidf')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print('BRAND :',data['brand'].loc[df_indices[i]])
        print ('Eucliden distance from the given image :', pdists[i])
        print('='*125)
tfidf_model(12545, 20)
# in the output heat map each value represents the tfidf values of the label word, the color represents the intersection with inputs title
ASIN : B00X50QQDC
BRAND : Crazy
Eucliden distance from the given image : 0.0
=============================================================================================================================
ASIN : B00X3SWT5A
BRAND : SYILA
Eucliden distance from the given image : 0.632952235182
=============================================================================================================================
ASIN : B01BWAUGYG
BRAND : OURS
Eucliden distance from the given image : 0.956295614575
=============================================================================================================================
ASIN : B00UCVUVSI
BRAND : Aimadi
Eucliden distance from the given image : 1.00812929123
=============================================================================================================================
ASIN : B01226IFOC
BRAND : Sandistore
Eucliden distance from the given image : 1.08783487823
=============================================================================================================================
ASIN : B06XZZXJR3
BRAND : Charberry
Eucliden distance from the given image : 1.08787350359
=============================================================================================================================
ASIN : B01EFIHZ8M
BRAND : VEBE
Eucliden distance from the given image : 1.0960809133
=============================================================================================================================
ASIN : B071ZFKCJF
BRAND : GuPoBoU168
Eucliden distance from the given image : 1.09767461768
=============================================================================================================================
ASIN : B011RCJ9KG
BRAND : Chiclook Cool
Eucliden distance from the given image : 1.11599916974
=============================================================================================================================
ASIN : B075451TLG
BRAND : Rbwinner
Eucliden distance from the given image : 1.11705391694
=============================================================================================================================
ASIN : B01F852VDK
BRAND : Perman
Eucliden distance from the given image : 1.1186293814
=============================================================================================================================
ASIN : B011OU53JC
BRAND : Chiclook Cool
Eucliden distance from the given image : 1.12022425553
=============================================================================================================================
ASIN : B00Z6HG4Z2
BRAND : Black Temptation
Eucliden distance from the given image : 1.12377702465
=============================================================================================================================
ASIN : B00VYJX4OU
BRAND : YICHUN
Eucliden distance from the given image : 1.12379238882
=============================================================================================================================
ASIN : B00UCVVSUI
BRAND : Aimadi
Eucliden distance from the given image : 1.12496660416
=============================================================================================================================
ASIN : B00P1I5TNE
BRAND : Huawen
Eucliden distance from the given image : 1.12747120226
=============================================================================================================================
ASIN : B012VOJ1PM
BRAND : KM T-shirt
Eucliden distance from the given image : 1.12751798065
=============================================================================================================================
ASIN : B01LF90Q0I
BRAND : Doxi Supermall
Eucliden distance from the given image : 1.12770031144
=============================================================================================================================
ASIN : B01LY4GQY0
BRAND : Doxi Supermall
Eucliden distance from the given image : 1.12777522425
=============================================================================================================================
ASIN : B01LF90SL0
BRAND : Doxi Supermall
Eucliden distance from the given image : 1.12777522425
=============================================================================================================================

[8.5] IDF based product similarity

In [16]:
idf_title_vectorizer = CountVectorizer()
idf_title_features = idf_title_vectorizer.fit_transform(data['title'])

# idf_title_features.shape = #data_points * #words_in_corpus
# CountVectorizer().fit_transform(courpus) returns the a sparase matrix of dimensions #data_points * #words_in_corpus
# idf_title_features[doc_id, index_of_word_in_corpus] = number of times the word occured in that doc
In [17]:
def n_containing(word):
    # return the number of documents which had the given word
    return sum(1 for blob in data['title'] if word in blob.split())

def idf(word):
    # idf = log(#number of docs / #number of docs which had the given word)
    return math.log(data.shape[0] / (n_containing(word)))
In [18]:
# we need to convert the values into float
idf_title_features  = idf_title_features.astype(np.float)

for i in idf_title_vectorizer.vocabulary_.keys():
    # for every word in whole corpus we will find its idf value
    idf_val = idf(i)
    
    # to calculate idf_title_features we need to replace the count values with the idf values of the word
    # idf_title_features[:, idf_title_vectorizer.vocabulary_[i]].nonzero()[0] will return all documents in which the word i present
    for j in idf_title_features[:, idf_title_vectorizer.vocabulary_[i]].nonzero()[0]:
        
        # we replace the count values of word i in document j with  idf_value of word i 
        # idf_title_features[doc_id, index_of_word_in_courpus] = idf value of word
        idf_title_features[j,idf_title_vectorizer.vocabulary_[i]] = idf_val
        
In [19]:
def idf_model(doc_id, num_results):
    # doc_id: apparel's id in given corpus
    
    # pairwise_dist will store the distance from given input apparel to all remaining apparels
    # the metric we used here is cosine, the coside distance is mesured as K(X, Y) = <X, Y> / (||X||*||Y||)
    # http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity
    pairwise_dist = pairwise_distances(idf_title_features,idf_title_features[doc_id])

    # np.argsort will return indices of 9 smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the 9 smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])

    for i in range(0,len(indices)):
        get_result(indices[i],data['title'].loc[df_indices[0]], data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], 'idf')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print('Brand :',data['brand'].loc[df_indices[i]])
        print ('euclidean distance from the given image :', pdists[i])
        print('='*125)

        
        
idf_model(12566,20)
# in the output heat map each value represents the idf values of the label word, the color represents the intersection with inputs title
ASIN : B00JXQB5FQ
Brand : Si Row
euclidean distance from the given image : 0.0
=============================================================================================================================
ASIN : B00JXQASS6
Brand : Si Row
euclidean distance from the given image : 12.2050713112
=============================================================================================================================
ASIN : B00JXQCWTO
Brand : Si Row
euclidean distance from the given image : 14.4683626856
=============================================================================================================================
ASIN : B00JXQAFZ2
Brand : Si Row
euclidean distance from the given image : 14.4868329248
=============================================================================================================================
ASIN : B00JXQAO94
Brand : Si Row
euclidean distance from the given image : 14.8333929667
=============================================================================================================================
ASIN : B00JXQCUIC
Brand : Si Row
euclidean distance from the given image : 14.8987445167
=============================================================================================================================
ASIN : B00JXQAUWA
Brand : Si Row
euclidean distance from the given image : 15.2244582873
=============================================================================================================================
ASIN : B074T8ZYGX
Brand : MKP Crop Top
euclidean distance from the given image : 17.0808129556
=============================================================================================================================
ASIN : B00KF2N5PU
Brand : Vietsbay
euclidean distance from the given image : 17.0901681256
=============================================================================================================================
ASIN : B00JPOZ9GM
Brand : Sofra
euclidean distance from the given image : 17.1532153376
=============================================================================================================================
ASIN : B074T9KG9Q
Brand : Rain
euclidean distance from the given image : 17.3367152387
=============================================================================================================================
ASIN : B00H8A6ZLI
Brand : Vivian's Fashions
euclidean distance from the given image : 17.410075941
=============================================================================================================================
ASIN : B074G5G5RK
Brand : ERMANNO SCERVINO
euclidean distance from the given image : 17.5399213355
=============================================================================================================================
ASIN : B06XSCVFT5
Brand : Studio M
euclidean distance from the given image : 17.6127585437
=============================================================================================================================
ASIN : B06Y6FH453
Brand : Who What Wear
euclidean distance from the given image : 17.6237452825
=============================================================================================================================
ASIN : B074V45DCX
Brand : Rain
euclidean distance from the given image : 17.6343424968
=============================================================================================================================
ASIN : B07583CQFT
Brand : Very J
euclidean distance from the given image : 17.6375371274
=============================================================================================================================
ASIN : B073GJGVBN
Brand : Ivan Levi
euclidean distance from the given image : 17.7230738913
=============================================================================================================================
ASIN : B012VQLT6Y
Brand : KM T-shirt
euclidean distance from the given image : 17.7625885612
=============================================================================================================================
ASIN : B00ZZMYBRG
Brand : HP-LEISURE
euclidean distance from the given image : 17.7795368647
=============================================================================================================================

Text Semantics based product similarity

In [24]:
#from gensim.models import Word2Vec
#from gensim.models import KeyedVectors
import pickle

# in this project we are using a pretrained model by google
# its 3.3G file, once you load this into your memory 
# it occupies ~9Gb, so please do this step only if you have >12G of ram

'''
model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
'''

#In case of not having RAM >= 12GB, use the code below.
with open('word2vec_model', 'rb') as handle:
    model = pickle.load(handle)
In [25]:
# Utility functions

def get_word_vec(sentence, doc_id, m_name):
    # sentence : title of the apparel
    # doc_id: document id in our corpus
    # m_name: model information it will take two values
        # if  m_name == 'avg', we will append the model[i], w2v representation of word i
        # if m_name == 'weighted', we will multiply each w2v[word] with the idf(word)
    vec = []
    for i in sentence.split():
        if i in vocab:
            if m_name == 'weighted' and i in  idf_title_vectorizer.vocabulary_:
                vec.append(idf_title_features[doc_id, idf_title_vectorizer.vocabulary_[i]] * model[i])
            elif m_name == 'avg':
                vec.append(model[i])
        else:
            # if the word in our courpus is not there in the google word2vec corpus, we are just ignoring it
            vec.append(np.zeros(shape=(300,)))
    # we will return a numpy array of shape (#number of words in title * 300 ) 300 = len(w2v_model[word])
    # each row represents the word2vec representation of each word (weighted/avg) in given sentance 
    return  np.array(vec)

def get_distance(vec1, vec2):
    # vec1 = np.array(#number_of_words_title1 * 300), each row is a vector of length 300 corresponds to each word in give title
    # vec2 = np.array(#number_of_words_title2 * 300), each row is a vector of length 300 corresponds to each word in give title
    
    final_dist = []
    # for each vector in vec1 we caluclate the distance(euclidean) to all vectors in vec2
    for i in vec1:
        dist = []
        for j in vec2:
            # np.linalg.norm(i-j) will result the euclidean distance between vectors i, j
            dist.append(np.linalg.norm(i-j))
        final_dist.append(np.array(dist))
    # final_dist = np.array(#number of words in title1 * #number of words in title2)
    # final_dist[i,j] = euclidean distance between vectors i, j
    return np.array(final_dist)


def heat_map_w2v(sentence1, sentence2, url, doc_id1, doc_id2, model):
    # sentance1 : title1, input apparel
    # sentance2 : title2, recommended apparel
    # url: apparel image url
    # doc_id1: document id of input apparel
    # doc_id2: document id of recommended apparel
    # model: it can have two values, 1. avg 2. weighted
    
    #s1_vec = np.array(#number_of_words_title1 * 300), each row is a vector(weighted/avg) of length 300 corresponds to each word in give title
    s1_vec = get_word_vec(sentence1, doc_id1, model)
    #s2_vec = np.array(#number_of_words_title1 * 300), each row is a vector(weighted/avg) of length 300 corresponds to each word in give title
    s2_vec = get_word_vec(sentence2, doc_id2, model)

    # s1_s2_dist = np.array(#number of words in title1 * #number of words in title2)
    # s1_s2_dist[i,j] = euclidean distance between words i, j
    s1_s2_dist = get_distance(s1_vec, s2_vec)

    
    
    # devide whole figure into 2 parts 1st part displays heatmap 2nd part displays image of apparel
    gs = gridspec.GridSpec(2, 2, width_ratios=[4,1],height_ratios=[2,1]) 
    fig = plt.figure(figsize=(15,15))
    
    ax = plt.subplot(gs[0])
    # ploting the heap map based on the pairwise distances
    ax = sns.heatmap(np.round(s1_s2_dist,4), annot=True)
    # set the x axis labels as recommended apparels title
    ax.set_xticklabels(sentence2.split())
    # set the y axis labels as input apparels title
    ax.set_yticklabels(sentence1.split())
    # set title as recommended apparels title
    ax.set_title(sentence2)
    
    ax = plt.subplot(gs[1])
    # we remove all grids and axis labels for image
    ax.grid(False)
    ax.set_xticks([])
    ax.set_yticks([])
    display_img(url, ax, fig)
    
    plt.show()
In [26]:
# vocab = stores all the words that are there in google w2v model
# vocab = model.wv.vocab.keys() # if you are using Google word2Vec

vocab = model.keys()
# this function will add the vectors of each word and returns the avg vector of given sentance
def build_avg_vec(sentence, num_features, doc_id, m_name):
    # sentace: its title of the apparel
    # num_features: the lenght of word2vec vector, its values = 300
    # m_name: model information it will take two values
        # if  m_name == 'avg', we will append the model[i], w2v representation of word i
        # if m_name == 'weighted', we will multiply each w2v[word] with the idf(word)

    featureVec = np.zeros((num_features,), dtype="float32")
    # we will intialize a vector of size 300 with all zeros
    # we add each word2vec(wordi) to this fetureVec
    nwords = 0
    
    for word in sentence.split():
        nwords += 1
        if word in vocab:
            if m_name == 'weighted' and word in  idf_title_vectorizer.vocabulary_:
                featureVec = np.add(featureVec, idf_title_features[doc_id, idf_title_vectorizer.vocabulary_[word]] * model[word])
            elif m_name == 'avg':
                featureVec = np.add(featureVec, model[word])
    if(nwords>0):
        featureVec = np.divide(featureVec, nwords)
    # returns the avg vector of given sentance, its of shape (1, 300)
    return featureVec

Average Word2Vec product similarity.

In [27]:
doc_id = 0
w2v_title = []
# for every title we build a avg vector representation
for i in data['title']:
    w2v_title.append(build_avg_vec(i, 300, doc_id,'avg'))
    doc_id += 1

# w2v_title = np.array(# number of doc in courpus * 300), each row corresponds to a doc 
w2v_title = np.array(w2v_title)
In [28]:
def avg_w2v_model(doc_id, num_results):
    # doc_id: apparel's id in given corpus
    
    # dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y))
    pairwise_dist = pairwise_distances(w2v_title, w2v_title[doc_id].reshape(1,-1))

    # np.argsort will return indices of 9 smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the 9 smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])
    
    for i in range(0, len(indices)):
        heat_map_w2v(data['title'].loc[df_indices[0]],data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], indices[0], indices[i], 'avg')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print('BRAND :',data['brand'].loc[df_indices[i]])
        print ('euclidean distance from given input image :', pdists[i])
        print('='*125)

        
avg_w2v_model(12566, 20)
# in the give heat map, each cell contains the euclidean distance between words i, j
ASIN : B00JXQB5FQ
BRAND : Si Row
euclidean distance from given input image : 0.000690534
=============================================================================================================================
ASIN : B00JXQASS6
BRAND : Si Row
euclidean distance from given input image : 0.589193
=============================================================================================================================
ASIN : B00JXQCWTO
BRAND : Si Row
euclidean distance from given input image : 0.700344
=============================================================================================================================
ASIN : B00JXQAFZ2
BRAND : Si Row
euclidean distance from given input image : 0.89284
=============================================================================================================================
ASIN : B00JXQCUIC
BRAND : Si Row
euclidean distance from given input image : 0.956013
=============================================================================================================================
ASIN : B073R5Q8HD
BRAND : Colosseum
euclidean distance from given input image : 1.02297
=============================================================================================================================
ASIN : B06XBY5QXL
BRAND : Liz Claiborne
euclidean distance from given input image : 1.06693
=============================================================================================================================
ASIN : B01L8L73M2
BRAND : Hotgirl4 Raglan Design
euclidean distance from given input image : 1.07314
=============================================================================================================================
ASIN : B01EJS5H06
BRAND : Vansty
euclidean distance from given input image : 1.07572
=============================================================================================================================
ASIN : B01BO1XRK8
BRAND : Le Bos
euclidean distance from given input image : 1.084
=============================================================================================================================
ASIN : B072R2JXKW
BRAND : WHAT ON EARTH
euclidean distance from given input image : 1.08422
=============================================================================================================================
ASIN : B074MJRGW6
BRAND : Two by Vince Camuto
euclidean distance from given input image : 1.0895
=============================================================================================================================
ASIN : B00JXQCFRS
BRAND : Si Row
euclidean distance from given input image : 1.09006
=============================================================================================================================
ASIN : B01I53HU6K
BRAND : ouxiuli
euclidean distance from given input image : 1.09201
=============================================================================================================================
ASIN : B0711NGTQM
BRAND : THILFIGER RTW
euclidean distance from given input image : 1.09234
=============================================================================================================================
ASIN : B01EFSLO8Y
BRAND : Vansty
euclidean distance from given input image : 1.0934
=============================================================================================================================
ASIN : B0716TVWQ4
BRAND : THILFIGER RTW
euclidean distance from given input image : 1.0942
=============================================================================================================================
ASIN : B0716MVPGV
BRAND : V.Secret
euclidean distance from given input image : 1.09483
=============================================================================================================================
ASIN : B016OPN4OI
BRAND : TIKE Fashions
euclidean distance from given input image : 1.09513
=============================================================================================================================
ASIN : B018WDJCUA
BRAND : INC - International Concepts Woman
euclidean distance from given input image : 1.09669
=============================================================================================================================

IDF weighted Word2Vec for product similarity

In [29]:
doc_id = 0
w2v_title_weight = []
# for every title we build a weighted vector representation
for i in data['title']:
    w2v_title_weight.append(build_avg_vec(i, 300, doc_id,'weighted'))
    doc_id += 1
# w2v_title = np.array(# number of doc in courpus * 300), each row corresponds to a doc 
w2v_title_weight = np.array(w2v_title_weight)
In [30]:
def weighted_w2v_model(doc_id, num_results):
    # doc_id: apparel's id in given corpus
    
    # pairwise_dist will store the distance from given input apparel to all remaining apparels
    # the metric we used here is cosine, the coside distance is mesured as K(X, Y) = <X, Y> / (||X||*||Y||)
    # http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity
    pairwise_dist = pairwise_distances(w2v_title_weight, w2v_title_weight[doc_id].reshape(1,-1))

    # np.argsort will return indices of 9 smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the 9 smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])
    
    for i in range(0, len(indices)):
        heat_map_w2v(data['title'].loc[df_indices[0]],data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], indices[0], indices[i], 'weighted')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print('Brand :',data['brand'].loc[df_indices[i]])
        print('euclidean distance from input :', pdists[i])
        print('='*125)

weighted_w2v_model(12566, 20)
#931
#12566
# in the give heat map, each cell contains the euclidean distance between words i, j
ASIN : B00JXQB5FQ
Brand : Si Row
euclidean distance from input : 0.00390625
=============================================================================================================================
ASIN : B00JXQASS6
Brand : Si Row
euclidean distance from input : 4.06389
=============================================================================================================================
ASIN : B00JXQCWTO
Brand : Si Row
euclidean distance from input : 4.77094
=============================================================================================================================
ASIN : B00JXQAFZ2
Brand : Si Row
euclidean distance from input : 5.36016
=============================================================================================================================
ASIN : B00JXQAUWA
Brand : Si Row
euclidean distance from input : 5.68952
=============================================================================================================================
ASIN : B00JXQAO94
Brand : Si Row
euclidean distance from input : 5.69302
=============================================================================================================================
ASIN : B00JXQCUIC
Brand : Si Row
euclidean distance from input : 5.89344
=============================================================================================================================
ASIN : B015H41F6G
Brand : KINGDE
euclidean distance from input : 6.13299
=============================================================================================================================
ASIN : B073R5Q8HD
Brand : Colosseum
euclidean distance from input : 6.25671
=============================================================================================================================
ASIN : B074P8MD22
Brand : Edista
euclidean distance from input : 6.3922
=============================================================================================================================
ASIN : B00C0I3U3E
Brand : Stanzino
euclidean distance from input : 6.4149
=============================================================================================================================
ASIN : B073R4ZM7Y
Brand : Colosseum
euclidean distance from input : 6.45096
=============================================================================================================================
ASIN : B01C6ORLDQ
Brand : 1 Mad Fit
euclidean distance from input : 6.46341
=============================================================================================================================
ASIN : B06XBY5QXL
Brand : Liz Claiborne
euclidean distance from input : 6.53922
=============================================================================================================================
ASIN : B071YF3WDD
Brand : Merona
euclidean distance from input : 6.5755
=============================================================================================================================
ASIN : B00H8A6ZLI
Brand : Vivian's Fashions
euclidean distance from input : 6.63822
=============================================================================================================================
ASIN : B00Z6HEXWI
Brand : Black Temptation
euclidean distance from input : 6.66074
=============================================================================================================================
ASIN : B00ILGH5OY
Brand : Ralph Lauren Active
euclidean distance from input : 6.68391
=============================================================================================================================
ASIN : B06Y1VN8WQ
Brand : Black Swan
euclidean distance from input : 6.70576
=============================================================================================================================
ASIN : B00KSNTY7Y
Brand : Anna-Kaci
euclidean distance from input : 6.70612
=============================================================================================================================

Weighted similarity using brand and color.

In [31]:
# some of the brand values are empty. 
# Need to replace Null with string "NULL"
data['brand'].fillna(value="Not given", inplace=True )

# replace spaces with hypen
brands = [x.replace(" ", "-") for x in data['brand'].values]
types = [x.replace(" ", "-") for x in data['product_type_name'].values]
colors = [x.replace(" ", "-") for x in data['color'].values]

brand_vectorizer = CountVectorizer()
brand_features = brand_vectorizer.fit_transform(brands)

type_vectorizer = CountVectorizer()
type_features = type_vectorizer.fit_transform(types)

color_vectorizer = CountVectorizer()
color_features = color_vectorizer.fit_transform(colors)

extra_features = hstack((brand_features, type_features, color_features)).tocsr()
In [32]:
def heat_map_w2v_brand(sentance1, sentance2, url, doc_id1, doc_id2, df_id1, df_id2, model):
    
    # sentance1 : title1, input apparel
    # sentance2 : title2, recommended apparel
    # url: apparel image url
    # doc_id1: document id of input apparel
    # doc_id2: document id of recommended apparel
    # df_id1: index of document1 in the data frame
    # df_id2: index of document2 in the data frame
    # model: it can have two values, 1. avg 2. weighted
    
    #s1_vec = np.array(#number_of_words_title1 * 300), each row is a vector(weighted/avg) of length 300 corresponds to each word in give title
    s1_vec = get_word_vec(sentance1, doc_id1, model)
    #s2_vec = np.array(#number_of_words_title2 * 300), each row is a vector(weighted/avg) of length 300 corresponds to each word in give title
    s2_vec = get_word_vec(sentance2, doc_id2, model)
    
    # s1_s2_dist = np.array(#number of words in title1 * #number of words in title2)
    # s1_s2_dist[i,j] = euclidean distance between words i, j
    s1_s2_dist = get_distance(s1_vec, s2_vec)
   
    data_matrix = [['Asin','Brand', 'Color', 'Product type'],
               [data['asin'].loc[df_id1],brands[doc_id1], colors[doc_id1], types[doc_id1]], # input apparel's features
               [data['asin'].loc[df_id2],brands[doc_id2], colors[doc_id2], types[doc_id2]]] # recommonded apparel's features
    
    colorscale = [[0, '#1d004d'],[.5, '#f2e5ff'],[1, '#f2e5d1']] # to color the headings of each column 
    
    # we create a table with the data_matrix
    table = ff.create_table(data_matrix, index=True, colorscale=colorscale)
    # plot it with plotly
    plotly.offline.iplot(table, filename='simple_table')
    
    # devide whole figure space into 25 * 1:10 grids
    gs = gridspec.GridSpec(25, 15)
    fig = plt.figure(figsize=(25,5))
    
    # in first 25*10 grids we plot heatmap
    ax1 = plt.subplot(gs[:, :-5])
    # ploting the heap map based on the pairwise distances
    ax1 = sns.heatmap(np.round(s1_s2_dist,6), annot=True)
    # set the x axis labels as recommended apparels title
    ax1.set_xticklabels(sentance2.split())
    # set the y axis labels as input apparels title
    ax1.set_yticklabels(sentance1.split())
    # set title as recommended apparels title
    ax1.set_title(sentance2)

    # in last 25 * 10:15 grids we display image
    ax2 = plt.subplot(gs[:, 10:16])
    # we dont display grid lins and axis labels to images
    ax2.grid(False)
    ax2.set_xticks([])
    ax2.set_yticks([])
    
    # pass the url it display it
    display_img(url, ax2, fig)
    
    plt.show()
In [33]:
def idf_w2v_brand(doc_id, w1, w2, num_results):
    # doc_id: apparel's id in given corpus
    # w1: weight for  w2v features
    # w2: weight for brand and color features

    # pairwise_dist will store the distance from given input apparel to all remaining apparels
    # the metric we used here is cosine, the coside distance is mesured as K(X, Y) = <X, Y> / (||X||*||Y||)
    # http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity
    idf_w2v_dist  = pairwise_distances(w2v_title_weight, w2v_title_weight[doc_id].reshape(1,-1))
    ex_feat_dist = pairwise_distances(extra_features, extra_features[doc_id])
    pairwise_dist   = (w1 * idf_w2v_dist +  w2 * ex_feat_dist)/float(w1 + w2)

    # np.argsort will return indices of 9 smallest distances
    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    #pdists will store the 9 smallest distances
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    #data frame indices of the 9 smallest distace's
    df_indices = list(data.index[indices])
    

    for i in range(0, len(indices)):
        heat_map_w2v_brand(data['title'].loc[df_indices[0]],data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], indices[0], indices[i],df_indices[0], df_indices[i], 'weighted')
        print('ASIN :',data['asin'].loc[df_indices[i]])
        print('Brand :',data['brand'].loc[df_indices[i]])
        print('euclidean distance from input :', pdists[i])
        print('='*125)

idf_w2v_brand(12566, 5, 5, 20)
# in the give heat map, each cell contains the euclidean distance between words i, j
ASIN : B00JXQB5FQ
Brand : Si Row
euclidean distance from input : 0.001953125
=============================================================================================================================
ASIN : B00JXQCWTO
Brand : Si Row
euclidean distance from input : 2.38547115326
=============================================================================================================================
ASIN : B00JXQASS6
Brand : Si Row
euclidean distance from input : 2.73905105609
=============================================================================================================================
ASIN : B00JXQAFZ2
Brand : Si Row
euclidean distance from input : 3.387187195
=============================================================================================================================
ASIN : B00JXQAUWA
Brand : Si Row
euclidean distance from input : 3.55186862964
=============================================================================================================================
ASIN : B00JXQAO94
Brand : Si Row
euclidean distance from input : 3.5536174776
=============================================================================================================================
ASIN : B00JXQCUIC
Brand : Si Row
euclidean distance from input : 3.65382804889
=============================================================================================================================
ASIN : B00JXQCFRS
Brand : Si Row
euclidean distance from input : 4.12881164569
=============================================================================================================================
ASIN : B00JXQC8L6
Brand : Si Row
euclidean distance from input : 4.20390014667
=============================================================================================================================
ASIN : B00JV63CW2
Brand : Si Row
euclidean distance from input : 4.28658676166
=============================================================================================================================
ASIN : B015H41F6G
Brand : KINGDE
euclidean distance from input : 4.38937059724
=============================================================================================================================
ASIN : B00JXQBBMI
Brand : Si Row
euclidean distance from input : 4.39791030902
=============================================================================================================================
ASIN : B073R5Q8HD
Brand : Colosseum
euclidean distance from input : 4.45122896516
=============================================================================================================================
ASIN : B074P8MD22
Brand : Edista
euclidean distance from input : 4.51897779787
=============================================================================================================================
ASIN : B00JV63QQE
Brand : Si Row
euclidean distance from input : 4.52937507647
=============================================================================================================================
ASIN : B00C0I3U3E
Brand : Stanzino
euclidean distance from input : 4.53032614076
=============================================================================================================================
ASIN : B01ER184O6
Brand : GuPoBoU168
euclidean distance from input : 4.54681702403
=============================================================================================================================
ASIN : B073R4ZM7Y
Brand : Colosseum
euclidean distance from input : 4.54835554445
=============================================================================================================================
ASIN : B071YF3WDD
Brand : Merona
euclidean distance from input : 4.61062742555
=============================================================================================================================
ASIN : B01C6ORLDQ
Brand : 1 Mad Fit
euclidean distance from input : 4.64591827429
=============================================================================================================================
In [34]:
# brand and color weight =50
# title vector weight = 5

idf_w2v_brand(12566, 5, 50, 20)
ASIN : B00JXQB5FQ
Brand : Si Row
euclidean distance from input : 0.000355113636364
=============================================================================================================================
ASIN : B00JXQCWTO
Brand : Si Row
euclidean distance from input : 0.433722027865
=============================================================================================================================
ASIN : B00JXQASS6
Brand : Si Row
euclidean distance from input : 1.65509310669
=============================================================================================================================
ASIN : B00JXQAFZ2
Brand : Si Row
euclidean distance from input : 1.77293604103
=============================================================================================================================
ASIN : B00JXQAUWA
Brand : Si Row
euclidean distance from input : 1.80287812006
=============================================================================================================================
ASIN : B00JXQAO94
Brand : Si Row
euclidean distance from input : 1.80319609241
=============================================================================================================================
ASIN : B00JXQCUIC
Brand : Si Row
euclidean distance from input : 1.82141619628
=============================================================================================================================
ASIN : B00JXQCFRS
Brand : Si Row
euclidean distance from input : 1.90777685025
=============================================================================================================================
ASIN : B00JXQC8L6
Brand : Si Row
euclidean distance from input : 1.92142930497
=============================================================================================================================
ASIN : B00JV63CW2
Brand : Si Row
euclidean distance from input : 1.93646323497
=============================================================================================================================
ASIN : B00JXQBBMI
Brand : Si Row
euclidean distance from input : 1.95670387995
=============================================================================================================================
ASIN : B00JV63QQE
Brand : Si Row
euclidean distance from input : 1.98060656494
=============================================================================================================================
ASIN : B00JV63VC8
Brand : Si Row
euclidean distance from input : 2.01218559992
=============================================================================================================================
ASIN : B00JXQAX2C
Brand : Si Row
euclidean distance from input : 2.01335178755
=============================================================================================================================
ASIN : B00JXQC0C8
Brand : Si Row
euclidean distance from input : 2.01388334827
=============================================================================================================================
ASIN : B00JXQABB0
Brand : Si Row
euclidean distance from input : 2.03672582486
=============================================================================================================================
ASIN : B01ER184O6
Brand : GuPoBoU168
euclidean distance from input : 2.65620416778
=============================================================================================================================
ASIN : B01LZ7BQ4H
Brand : WAYF
euclidean distance from input : 2.6849067823
=============================================================================================================================
ASIN : B01KJUM6JI
Brand : YABINA
euclidean distance from input : 2.68583819266
=============================================================================================================================
ASIN : B01M06V4X1
Brand : WAYF
euclidean distance from input : 2.69476194865
=============================================================================================================================

Keras and Tensorflow to extract features

In [35]:
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
from sklearn.metrics import pairwise_distances
import matplotlib.pyplot as plt
import requests
from PIL import Image
import pandas as pd
import pickle
Using TensorFlow backend.
In [ ]:
# https://gist.github.com/fchollet/f35fbc80e066a49d65f1688a7e99f069
# Code reference: https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html



# This code takes 40 minutes to run on a modern GPU (graphics card) 
# like Nvidia  1050.
# GPU (NVidia 1050): 0.175 seconds per image

# This codse takes 160 minutes to run on a high end i7 CPU
# CPU (i7): 0.615 seconds per image.

#Do NOT run this code unless you want to wait a few hours for it to generate output

# each image is converted into 25088 length dense-vector

# dimensions of our images.
img_width, img_height = 224, 224

top_model_weights_path = 'bottleneck_fc_model.h5'
train_data_dir = 'images2/'
nb_train_samples = 16042
epochs = 50
batch_size = 1


def save_bottlebeck_features():
    
    #Function to compute VGG-16 CNN for image feature extraction.
    
    asins = []
    datagen = ImageDataGenerator(rescale=1. / 255)
    
    # build the VGG16 network
    model = applications.VGG16(include_top=False, weights='imagenet')
    generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

    for i in generator.filenames:
        asins.append(i[2:-5])

    bottleneck_features_train = model.predict_generator(generator, nb_train_samples // batch_size)
    bottleneck_features_train = bottleneck_features_train.reshape((16042,25088))
    
    np.save(open('16k_data_cnn_features.npy', 'wb'), bottleneck_features_train)
    np.save(open('16k_data_cnn_feature_asins.npy', 'wb'), np.array(asins))
    

save_bottlebeck_features()

Visual features based product similarity.

In [ ]:
#load the features and corresponding ASINS info.
bottleneck_features_train = np.load('16k_data_cnn_features.npy')
asins = np.load('16k_data_cnn_feature_asins.npy')
asins = list(asins)

# load the original 16K dataset
data = pd.read_pickle('pickels/16k_apperal_data_preprocessed')
df_asins = list(data['asin'])


from IPython.display import display, Image, SVG, Math, YouTubeVideo


#get similar products using CNN features (VGG-16)
def get_similar_products_cnn(doc_id, num_results):
    doc_id = asins.index(df_asins[doc_id])
    pairwise_dist = pairwise_distances(bottleneck_features_train, bottleneck_features_train[doc_id].reshape(1,-1))

    indices = np.argsort(pairwise_dist.flatten())[0:num_results]
    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]

    for i in range(len(indices)):
        rows = data[['medium_image_url','title']].loc[data['asin']==asins[indices[i]]]
        for indx, row in rows.iterrows():
            display(Image(url=row['medium_image_url'], embed=True))
            print('Product Title: ', row['title'])
            print('Euclidean Distance from input image:', pdists[i])
            print('Amazon Url: www.amzon.com/dp/'+ asins[indices[i]])

get_similar_products_cnn(12566, 20)